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EarlyStopping

class pytorch_lightning.callbacks.EarlyStopping(monitor='early_stop_on', min_delta=0.0, patience=3, verbose=False, mode='min', strict=True)[source]

Bases: pytorch_lightning.callbacks.base.Callback

Monitor a metric and stop training when it stops improving.

Parameters
  • monitor (str) – quantity to be monitored.

  • min_delta (float) – minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.

  • patience (int) –

    number of validation checks with no improvement after which training will be stopped. Under the default configuration, one validation check happens after every training epoch. However, the frequency of validation can be modified by setting various parameters on the Trainer, for example check_val_every_n_epoch and val_check_interval.

    Note

    It must be noted that the patience parameter counts the number of validation checks with no improvement, and not the number of training epochs. Therefore, with parameters check_val_every_n_epoch=10 and patience=3, the trainer will perform at least 40 training epochs before being stopped.

  • verbose (bool) – verbosity mode.

  • mode (str) – one of 'min', 'max'. In 'min' mode, training will stop when the quantity monitored has stopped decreasing and in 'max' mode it will stop when the quantity monitored has stopped increasing.

  • strict (bool) – whether to crash the training if monitor is not found in the validation metrics.

Raises
  • MisconfigurationException – If mode is none of "min" or "max".

  • RuntimeError – If the metric monitor is not available.

Example:

>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.callbacks import EarlyStopping
>>> early_stopping = EarlyStopping('val_loss')
>>> trainer = Trainer(callbacks=[early_stopping])
on_load_checkpoint(callback_state)[source]

Called when loading a model checkpoint, use to reload state.

Parameters

callback_state (Dict[str, Any]) – the callback state returned by on_save_checkpoint.

on_save_checkpoint(trainer, pl_module, checkpoint)[source]

Called when saving a model checkpoint, use to persist state.

Parameters
  • trainer – the current Trainer instance.

  • pl_module – the current LightningModule instance.

  • checkpoint (Dict[str, Any]) – the checkpoint dictionary that will be saved.

Return type

Dict[str, Any]

Returns

The callback state.

on_validation_end(trainer, pl_module)[source]

Called when the validation loop ends.

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